Physical database design for relational databases
ACM Transactions on Database Systems (TODS)
Balancing histogram optimality and practicality for query result size estimation
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Approximate medians and other quantiles in one pass and with limited memory
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Random sampling for histogram construction: how much is enough?
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Towards estimation error guarantees for distinct values
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Adaptive and Automated Index Selection in RDBMS
EDBT '92 Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database Technology
Physical Database Design for Data Warehouses
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Massive Stochastic Testing of SQL
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Sampling-Based Estimation of the Number of Distinct Values of an Attribute
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Fast Incremental Maintenance of Approximate Histograms
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Index Selection in Relational Databases
ICCI '93 Proceedings of the Fifth International Conference on Computing and Information
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
A characterization of the sensitivity of query optimization to storage access cost parameters
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
GORDIAN: efficient and scalable discovery of composite keys
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Automated statistics collection in DB2 UDB
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Detecting attribute dependencies from query feedback
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Analytic-based estimation of query result sizes
AIKED'05 Proceedings of the 4th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering Data Bases
The Psychic-Skeptic Prediction framework for effective monitoring of DBMS workloads
Data & Knowledge Engineering
Statistical structures for Internet-scale data management
The VLDB Journal — The International Journal on Very Large Data Bases
StatAdvisor: recommending statistical views
Proceedings of the VLDB Endowment
Exact cardinality query optimization for optimizer testing
Proceedings of the VLDB Endowment
A sample advisor for approximate query processing
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Making updates disk-I/O friendly using SSDs
Proceedings of the VLDB Endowment
Optimizing Sample Design for Approximate Query Processing
International Journal of Knowledge-Based Organizations
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Statistics play a key role in influencing the quality of plans chosen by a database query optimizer. In this paper, we identify the statistics that are essential for an optimizer. We introduce novel techniques that help significantly reduce the set of statistics that need to be created without sacrificing the quality of query plans generated. We discuss how these techniques can be leveraged to automate statistics management in databases. We have implemented and experimentally evaluated our approach on Microsoft SQL Server 7.0.